| name | data-analysis |
| description | Analyze data, process CSV/JSON files, compute statistics, and create data visualizations. Use when the user asks about data processing, statistics, or analysis. |
| metadata | {"fastclaw":{"always":false}} |
Data Analysis Skill
Analyze and process data using Python in the sandbox.
Common Libraries
- pandas: DataFrames, CSV/JSON/Excel processing
- numpy: Numerical computing
- matplotlib: Visualization (use Agg backend)
Install if needed: pip install pandas numpy matplotlib
Common Tasks
Read and analyze CSV
import pandas as pd
df = pd.read_csv('data.csv')
print(df.describe())
print(f"\nShape: {df.shape}")
print(f"\nColumns: {list(df.columns)}")
print(f"\nFirst 5 rows:\n{df.head()}")
Create visualization from data
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import base64
df = pd.read_csv('data.csv')
df.plot(kind='bar', x='category', y='value', figsize=(10, 6))
plt.title('Data Overview')
plt.tight_layout()
plt.savefig('/tmp/chart.png', dpi=150)
with open('/tmp/chart.png', 'rb') as f:
print(f').decode()})')
JSON processing
import json
with open('data.json') as f:
data = json.load(f)
Guidelines
- Always execute the analysis — don't just show code
- Show key statistics: shape, dtypes, describe(), null counts
- For large datasets, show head/tail and summary stats
- Generate charts when it helps explain the data
- Use base64 inline images for any visualizations
